Proportional-Integral-Plus Control Strategy of an Intelligent Excavator

This article considers the application of Proportional-Integral-Plus (PIP) control to the Lancaster University Computerised Intelligent Excavator (LUCIE), which is being developed to dig foundation trenches on a building site. Previous work using LUCIE was based on the ubiquitous PI/PID control algorithm, tuned on-line, and implemented in a rather ad hoc manner. By contrast, the present research utilizes new hardware and advanced model-based control system design methods to improve the joint control and so provide smoother, more accurate movement of the excavator arm. In this article, a novel nonlinear simulation model of the system is developed for MATLAB/SIMULINK, allowing for straightforward refinement of the control algorithm and initial evaluation. The PIP controller is compared with a conventionally tuned PID algorithm, with the final designs implemented on-line for the control of dipper angle. The simulated responses and preliminary implementation results demonstrate the feasibility of the approach

Identification and energy optimization of supercritical carbon dioxide batch extraction

Abstract. The emergence of green chemistry, aiming to increase ecological and energy efficiency of processes, has gained supercritical fluid extraction increasing amounts of prominence. Traditional extraction methods utilize hazardous chemicals, have low extractive yield in relation to energy consumption, and produce large amounts of organic waste. Supercritical fluid extraction offers improvements to these challenges in the form of reduced processing energy inputs and an alternative solvent approach. Carbon dioxide is the most commonly employed solvent in supercritical fluid extraction due to the many advantages it brings over other solvents including price, smaller environmental and health risks, and simple separation. The research on data-driven system identification and advanced process control of supercritical extraction has been very scarce. According to past research, the control of supercritical is mostly carried out using basic, non-model-based control schemes. Challenges such as coupling between control loops and nonlinearities of fluid and process dynamics create major challenges for the basic control schemes. With advanced control methods, it could be possible to address these challenges better. Model-based control schemes, in theory, pose many advantages and benefits over basic control, such as improved production economics, optimized product quality and yields, and further possibilities in model-driven research and development. The goal of this thesis was to improve control performance and optimize energy consumption a pilot-scale batch supercritical carbon dioxide extraction process by utilizing model predictive control strategies. The modeling of the unit processes of the target batch extraction was based on measurement data gathered by experimental design and careful examination of the system. The models were utilized in a simulator developed in this study. The arrangement of the implemented experimental design (central composite design, CCD) allowed the exploitation of linear regression analysis; the results of which indicated the existence of possible nonlinearities between steady-state electricity consumption and the operative variables of the process. Model predictive control schemes were developed in a simulator environment for carbon dioxide pressure control, carbon dioxide volumetric flow control, extractor temperature control and separator temperature control. The developed control schemes showed major improvements in control performance of the simulated unit processes, resulting in significant decreases in total electricity and heating water consumptions (up to 25% and 21% respectively). Model predictive control also proved to be quite flexible over the base control system for some processes, providing the possibility of modifying control performance by simple tuning adjustments. The simulated control strategies demonstrate the benefits of model-based control in terms of process energy efficiency and economy. In addition to these results, the identified process and controller models have further potential in future research on control and process developments of supercritical fluid extraction.Ylikriittisen hiilidioksidipanosuuton identifiointi ja energiaoptimointi. Tiivistelmä. Prosessien ekologisuuden ja energiatehokkuuden lisäämiseen tähtäävä vihreä kemia edistää ylikriittisen uuton merkittävyyttä yhä enemmän. Perinteiset erotusmenetelmät käyttävät haitallisia kemikaaleja, niillä on alhainen uuteainesaanto suhteessa energian kulutukseen, ja ne tuottavat suuren määrän orgaanista jätettä. Ylikriittinen uutto tarjoaa parannuksia näihin haasteisiin prosessointienergian kulutuksen vähentymisen ja vaihtoehtoisen liuotinratkaisun muodossa. Hiilidioksidi on yleisimmin käytetty liuotin ylikriittisessä uutossa, koska sillä on monia etuja muihin liuottimiin verrattuna, mukaan lukien hinta, pienemmät ympäristö- ja terveysriskit sekä yksinkertainen erottaminen. Ylikriittiseen uuttoprosessiin liittyvän datapohjaisen identifioinnin ja kehittyneen säädön tutkimus on ollut hyvin vähäistä. Aiempien tutkimusten perusteella ylikriittisen uuton säätö toteutetaan pääasiassa perustason ei-mallipohjaisilla säätörakenteilla. Ohjaussilmukoiden vuorovaikutukset sekä neste- ja prosessidynamiikan epälineaarisuudet luovat suuria haasteita perussäätörakenteille. Kehittyneillä säätömenetelmillä olisi mahdollista käsitellä näitä haasteita paremmin. Mallipohjaiset säätöratkaisut tuovat teoriassa useita etuja ja hyötyjä perussäätöön verrattuna parantuvan tuotantoekonomian, optimoidun tuotelaadun ja -saannon sekä malliperusteisen tutkimuksen ja -kehityksen lisämahdollisuuksien muodossa. Tämän työn tavoitteena oli nostaa pilottikoon ylikriittisen hiilidioksidipanosuuttoprosessin säädön suorituskykyä ja optimoida energiankulutusta hyödyntämällä mallipredikriivisiä säätöstrategioita. Tutkimuksen kohteena olleen panosuuton yksikköprosessien mallinnus perustui koesuunnittelulla kerättyyn mittausaineistoon ja järjestelmän huolelliseen tarkkailuun. Malleja hyödynnettiin työssä kehitetyssä prosessisimulaattorissa. Toteutettu koessunnitelma (central composite design, CCD) mahdollisti lineaarisen regressioanalyysin hyödyntämisen, jonka tulokset osoittivat mahdollisten epälineaarisuuksien olemassaolon prosessin vakaan tilan sähkönkulutuksen ja operatiivisten muuttujien välillä. Malliprediktiiviset säätörakenteet kehitettiin simulaatioympäristössä hiilidioksidin paineen, hiilidioksidin tilavuusvirtauksen, uuttoreaktorin lämpötilan, ja erottajan lämpötilan säädöille. Kehitetyt säätörakenteet toivat suuria säätöparannuksia simuloituihin yksikköprosesseihin, johtaen merkittäviin vähennyksiin käyttösähkön- ja lämmitysveden kulutuksissa (vastaavat vähennykset 25 % ja 21 % saakka). Malliprediktiivinen säätö osoitti myös joustavuutensa perusäätöjärjestelmään verrattuna joissakin prosesseissa, mahdollistaen säätösuorituskyvyn modifioinnin yksinkertaisilla viritysmuutoksilla. Simuloidut säätöstrategiat havainnollistavat mallipohjaisen säädön mahdollisia hyötyjä prosessin energiatehokkuuden ja taloudellisuuden kannalta. Näiden tulosten lisäksi identifioiduilla prosessi- ja säädinmalleilla on lisäpotentiaalia tulevaisuuden ylikriittisen uuton säädön tutkimuksissa ja prosessikehityksissä

Robotics Vision-based Heuristic Reasoning for Underwater Target Tracking and Navigation

This paper presents a robotics vision-based heuristic reasoning system for underwater target tracking and navigation. This system is introduced to improve the level of automation of underwater Remote Operated Vehicles (ROVs) operations. A prototype which combines computer vision with an underwater robotics system is successfully designed and developed to perform target tracking and intelligent navigation. This study focuses on developing image processing algorithms and fuzzy inference system for the analysis of the terrain. The vision system developed is capable of interpreting underwater scene by extracting subjective uncertainties of the object of interest. Subjective uncertainties are further processed as multiple inputs of a fuzzy inference system that is capable of making crisp decisions concerning where to navigate. The important part of the image analysis is morphological filtering. The applications focus on binary images with the extension of gray-level concepts. An open-loop fuzzy control system is developed for classifying the traverse of terrain. The great achievement is the system's capability to recognize and perform target tracking of the object of interest (pipeline) in perspective view based on perceived condition. The effectiveness of this approach is demonstrated by computer and prototype simulations. This work is originated from the desire to develop robotics vision system with the ability to mimic the human expert's judgement and reasoning when maneuvering ROV in the traverse of the underwater terrain

OPERATION AND PROCESS CONTROL DEVELOPMENT FOR A PILOT-SCALE LEACHING AND SOLVENT EXTRACTION CIRCUIT RECOVERING RARE EARTH ELEMENTS FROM COAL-BASED SOURCES

The US Department of Energy in 2010 has identified several rare earth elements as critical materials to enable clean technologies. As part of ongoing research in REEs (rare earth elements) recovery from coal sources, the University of Kentucky has designed, developed and is demonstrating a ¼ ton/hour pilot-scale processing plant to produce high-grade REEs from coal sources. Due to the need to control critical variables (e.g. pH, tank level, etc.), process control is required. To ensure adequate process control, a study was conducted on leaching and solvent extraction control to evaluate the potential of achieving low-cost REE recovery in addition to developing a process control PLC system. The overall operational design and utilization of Six Sigma methodologies is discussed. Further, the application of the controls design, both procedural and electronic for the control of process variables such as pH is discussed. Variations in output parameters were quantified as a function of time. Data trends show that the mean process variable was maintained within prescribed limits. Future work for the utilization of data analysis and integration for data-based decision-making will be discussed

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

In this paper, Levenberg–Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor's control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg–Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions

Introduction of programmable logic controller in industrial engineering curriculum

Recent trends in process control and industrial automation scenarios have resulted in the emergence of many pioneering techniques that have revolutionized the manufacturing industry. In order to maintain quality and precision, advances have been associated with the increasing use of microprocessors in process control applications. Most of the industrial process control systems utilize Programmable Logic Controllers (PLC). Also due to the increase in internet usage and recent innovations in PLC software, remote monitoring and PLC control of process through the internet is also a recent trend. This thesis presents course/lab material for integration in the Industrial Engineering curriculum. The course/lab content was designed to improve the student\u27s knowledge and to broaden the industrial engineering curriculum at West Virginia University (WVU). This thesis proposes the use of inexpensive T100MD+ PLCs. A traffic light control system was developed to introduce the fundamental concepts of Boolean algebra and real-time control. A series of control exercises can be carried on the traffic light system. A temperature sensitive system was also developed. Students can test various PID control strategies on this hardware/software platform. Students will also have the ability to control the process via the internet

Force reflecting joystick control for applications to bilateral teleoperation in construction machinery

This paper presents a simple and effective force reflecting joystick controller for applications to bilateral teleoperation in construction machinery. First, this controller is a combination of an advanced force reflecting gain tuner and two local adaptive controllers, master and slave. Second, the force reflecting gain tuner is effectively designed using recursive least square method and fuzzy logics to estimate directly and accurately the environmental characteristics and, consequently, to produce properly a force reflection. Third, the local adaptive controllers are simply designed using fuzzy technique and optimized using a smart leaning mechanism to ensure that the slave follows well any given trajectory while the operator is able to achieve truly physical perception of interactions at the remote site. An experimental master-slave manipulator is setup and real-time control tests are carried out under various environmental conditions to evaluate the effectiveness of the proposed controller

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions

Review of Intelligent Control Systems with Robotics

Interactive between human and robot assumes a significant job in improving the productivity of the instrument in mechanical technology. Numerous intricate undertakings are cultivated continuously via self-sufficient versatile robots. Current automated control frameworks have upset the creation business, making them very adaptable and simple to utilize. This paper examines current and up and coming sorts of control frameworks and their execution in mechanical technology, and the job of AI in apply autonomy. It additionally expects to reveal insight into the different issues around the control frameworks and the various approaches to fix them. It additionally proposes the basics of apply autonomy control frameworks and various kinds of mechanical technology control frameworks. Each kind of control framework has its upsides and downsides which are talked about in this paper. Another kind of robot control framework that upgrades and difficulties the pursuit stage is man-made brainpower. A portion of the speculations utilized in man-made reasoning, for example, Artificial Intelligence (AI) such as fuzzy logic, neural network and genetic algorithm, are itemized in this paper. At long last, a portion of the joint efforts between mechanical autonomy, people, and innovation were referenced. Human coordinated effort, for example, Kinect signal acknowledgment utilized in games and versatile upper-arm-based robots utilized in the clinical field for individuals with inabilities. Later on, it is normal that the significance of different sensors will build, accordingly expanding the knowledge and activity of the robot in a modern domai